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Precise labeling of 3D object scales requires accurate spatial information, which is difficult to obtain from a single image due to the inherent lack of depth information in monocular images, compared to LiDAR data. In this paper, we propose a novel approach to address this issue by enhancing deep neural networks with depth information for monocular 3D object detection. The proposed method comprises three key components: 1)Feature Enhancement Pyramid Module: We extend the conventional Feature Pyramid Networks (FPN) by introducing a feature enhancement pyramid network. This module fuses feature maps from the original pyramid and captures contextual correlations across multiple scales. To increase the connectivity between low-level and high-level features, additional pathways are incorporated. 2)Auxiliary Dense Depth Estimator: We introduce an auxiliary dense depth estimator that generates dense depth maps to enhance the spatial perception capabilities of the deep network model without adding computational burden. 3)Augmented Center Depth Regression: To aid center depth estimation, we employ additional bounding box vertex depth regression based on geometry. Our experimental results demonstrate the superiority of the proposed technique over existing competitive methods reported in the literature. The approach showcases remarkable performance improvements in monocular 3D object detection, making it a promising solution for autonomous driving applications.<\/jats:p>","DOI":"10.1007\/s10846-024-02128-w","type":"journal-article","created":{"date-parts":[[2024,7,9]],"date-time":"2024-07-09T12:01:35Z","timestamp":1720526495000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Depth-Enhanced Deep Learning Approach For Monocular Camera Based 3D Object Detection"],"prefix":"10.1007","volume":"110","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-8060-6326","authenticated-orcid":false,"given":"Chuyao","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nabil","family":"Aouf","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,7,9]]},"reference":[{"key":"2128_CR1","doi-asserted-by":"crossref","unstructured":"He, L., Aouf, N., Whidborne, J.F., et al.: Integrated moment-based LGMD and deep reinforcement learning for UAV obstacle avoidance. 2020 IEEE International Conference on Robotics and Automation (ICRA), pp. 7491-7497, IEEE, (2020)","DOI":"10.1109\/ICRA40945.2020.9197152"},{"key":"2128_CR2","doi-asserted-by":"crossref","unstructured":"Shah, M.A., Aouf, N.: 3d cooperative pythagorean hodograph path planning and obstacle avoidance for multiple uavs. 2010 IEEE 9th International Conference on Cyberntic Intelligent Systems, pp. 1-6, IEEE, (2010)","DOI":"10.1109\/UKRICIS.2010.5898124"},{"issue":"16","key":"2128_CR3","doi-asserted-by":"publisher","first-page":"2877","DOI":"10.1177\/0954406220945468","volume":"235","author":"H Kanchwala","year":"2021","unstructured":"Kanchwala, H., Bezerra Viana, I., Aouf, N.: Cooperative path-planning and tracking controller evaluation using vehicle models of varying complexities. 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